RESUMO
Technological innovations that improve the speed, scale, reproducibility, and accuracy of monitoring surveys will allow for a better understanding of the global decline in tropical reef health. The DiveRay, a diver-operated hyperspectral imager, and a complementary machine learning pipeline to automate the analysis of hyperspectral imagery were developed for this purpose. To evaluate the use of a hyperspectral imager underwater, the automated classification of benthic taxa in reef communities was tested. Eight reefs in Guam were surveyed and two approaches for benthic classification were employed: high taxonomic resolution categories and broad benthic categories. The results from the DiveRay surveys were validated against data from concurrently conducted photoquadrat surveys to determine their accuracy and utility as a proxy for reef surveys. The high taxonomic resolution classifications did not reliably predict benthic communities when compared to those obtained by standard photoquadrat analysis. At the level of broad benthic categories, however, the hyperspectral results were comparable to those of the photoquadrat analysis. This was particularly true when estimating scleractinian coral cover, which was accurately predicted for six out of the eight sites. The annotation libraries generated for this study were insufficient to train the model to fully account for the high biodiversity on Guam's reefs. As such, prediction accuracy is expected to improve with additional surveying and image annotation. This study is the first to directly compare the results from underwater hyperspectral scanning with those from traditional photoquadrat survey techniques across multiple sites with two levels of identification resolution and different degrees of certainty. Our findings show that dependent on a well-annotated library, underwater hyperspectral imaging can be used to quickly, repeatedly, and accurately monitor and map dynamic benthic communities on tropical reefs using broad benthic categories.